Instructions to use dpredrag/AiGeneratorModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dpredrag/AiGeneratorModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dpredrag/AiGeneratorModel", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- d6cdf30d63ed94152881c25ec548622328a22e38ee4c0b1417c56b047190573d
- Size of remote file:
- 246 MB
- SHA256:
- 88ba06ea7892fcbcf970ad91d5f1f27cca8c028a631b93706c943ea50e5e52c1
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